New Weighted Mean-Based Patterns for Texture Analysis and Classification

Figures & data

Figure 1. An example that LBP is sensitive to noise

Figure 2. Illustration of WEM

Figure 3. Diagram of the proposed approach

Figure 4. The pseudo-code of the presented image retrieval scheme

Figure 5. Image samples of different image datasets in our experiments

Figure 6. Example of image retrieval results over different image datasets

Figure 7. The retrieval results in the proposed algorithm

Figure 8. The retrieved results after the first feedback iteration for an input image FKP (from hand dataset)

Table 1. The parameters and definitions

Parameter	Definition
WEM	$WE{M}_{P,T}=\sum _{i=0}^{P-1}\delta (g_i-T_{\hspace{0.17em}}){.2}^i$
Thresholds	$T=\frac{1}{Y}\sum _{i=1}^u\sum _{j=1}^v\hspace{0.17em}{G}_{i,j}\times K_{i,j}$
	$T_1=⌊M-\frac{S}{\sqrt{N_{DB}}}⌋$
	$T_2=⌈M+\frac{S}{\sqrt{N_{DB}}}⌉$
Kernels	$K_{i,j}=\left\{\begin{array}{c}1\begin{array}{cc}\hspace{0.17em}& \hspace{0.17em}\end{array}{G}_{i,j}>T_2\\ 2\begin{array}{cc}\hspace{0.17em}& \hspace{0.17em}\end{array}{G}_{i,j}<T_1\\ 3\begin{array}{cc}\hspace{0.17em}& \hspace{0.17em}\end{array}else\end{array}\right.$
The coordinates of the upper left corner of the sub-image related to the FKP	$x_1=min(x_C+\frac{L}{2}\times [cos(O)-cos(O)])$$y_1=min(y_C+\frac{L}{2}\times [-sin(O)sin(O)])$
	$x_2=x_1-ri(j)\times [cos(O)-cos(O)]$$y_2=y_1-ri(j)\times [sin(O)sin(O)]$ri = [120 200 230 230 180] O: the orientation of each finger L: the length of each finger j = 1,2,3,4,5 is the index of each finger
The distance metric between the query and target image	$\nu =\sum _{i=1}^{NF}{\omega }_i(\frac{WE{M}_{query}(i)-WE{M}_{target}(i)}{WE{M}_{query}(i)+WE{M}_{target}(i)})$
PSO	Max Fitness Function{q| ωi} q is the set of training images
SVM	$D(x)=W^{T}x+bi$W: the vector of separating hyper plane bi: the bias value
Evaluation Parameters	$Precision=\frac{TT}{TT+FT}$
	$Recall=\frac{TT}{TT+FN}$
	$ANMRR=\frac{1}{T_Q}\sum _{i=1}^{T_Q}NMR{R}_q$

Table 2. Performance comparison of the proposed approach with various approaches using average precision (%)

Different Methods	Average precision (%)
	Stex	Vistex	Brodatz	FKP
1	58.53	78.11	75.32	78.35
2	61.42	79.60	78.42	80.87
3	70.72	87.43	85.96	88.55
4	65.54	81.31	80.57	83.93
5	53.65	73.85	71.30	74.44
6	34.49	54.90	52.49	55.38
7	40.55	59.83	57.38	60.59
8	23.43	42.40	40.41	43.80
9	58.67	77.62	75.73	78.49
10	53.98	73.86	72.92	74.66
11	61.36	78.79	77.44	80.37
Ours	84.86	95.13	93.62	98.38

Figure 9. Comparison of the precision-recall obtained by presented method using different datasets

Table 3. Image retrieval performance comparisons of the proposed method

Different methods	Average precision (%)			Feature dimension
	Stex	Vistex	Brodatz
GG (Kwitt and Uhl 2010)	34.82	-	-	6
RCWT (Kokare, Biswas, and Chatterji 2005)	-	75.78	71.17	40
GT (Manjunath and Ma 1996)	-	76.57	17.19	48
GLBP (Guo, Zhang, and Zhang 2010)	-	84.74	75.21	3072
GLMEBP (Subrahmanyam, Maheswari, and Balasubramanian 2012)	-	87.93	82.01	6144
GLDP (Zhang et al. 2010)	-	88.18	79.24	236
LTrP (Murala, Maheshwari, and Balasubramanian 2012)	-	90.02	85.30	767
ODII (Guo, Prasetyo, and Su 2013)	-	93.23	85.56	256
Wbl (Kwitt and Uhl 2008)	34.18	-	-	6
Ours	84.86	95.13	97.62	125

Table 4. Times taken in different stages of hand image processing

Different stages	Times (ms)
Image loading	53
Preprocessing and FKP area extraction	219
Texture feature extraction	386
Feature selection	248
Matching	83

Figure 10. Times taken for feature extraction in milliseconds for FKP dataset

Table 5. Classification accuracy (%) for FKP, Brodatz, Vistex and Stex image datasets

Different methods	Classification accuracy (%)				Feature dimension
	Stex	Vistex	Brodatz	FKP
LBP+Haralick (Porebski, Vandenbroucke, and Macaire 2008)	-	91.59	-	-	10
MSD (Liu, Li, Lei and Xu 2011)	-	82.07	-	-	72
Multilayer CCR (Bianconi et al. 2009)	-	94.74	-	-	640
HRF (Paschos and Petrou 2003)	-	58.89	-	-	-
Gabor EEE (Hoang, Geusebroek, and Smeulders 2005)	-	96.92	-	-	192
Shortest graph (Junior, Cortex, and Backes 2014)	-	87.62	-	-	96
BRINT1_CS_CM (Liu et al. 2014)	-	-	99.78	-	144
CK-1 (Campana and Keogh 2010)	86	-	-	-	-
Sparse (Guha and Ward 2014)	84.50	-	-	-	-
SIFT (Lowe 2004)	52.7	-	-	-	-
WLD (Chen et al. 2010)	56.4	-	-	-	-
DRLBP (Satpathy, Jiang, and Eng 2014)	59	-	-	-	60
DRLTP (Satpathy, Jiang, and Eng 2014)	62.6	-	-	-	176
Ours	80.13	92.10	91.15	97.14	125

Table 6. Classification accuracy (%) for FKP, MRI, and StructureRSMAS image datasets using CNN, KNN, and SVM classifiers

Different methods	Classification accuracy (%)
	Classifier	StructureRSMAS	MRI	FKP
CBE (Rios et al. 2019)	CNN	83.684	-	-
Deblur (Rios et al. 2019)	CNN	85	-	-
Saliency (Rios et al. 2019)	CNN	52.632	-	-
TIORGW (Viji and Rajesh 2020)	KNN	-	96
Statistical texture feature (Ramteke and Monali 2012)	KNN	-	95
Ours	SVM	-	-	97.14

Figure 11. The ROC curve of the proposed biometric identification approach on the FKP dataset

Kwitt, R., and A. Uhl. 2010. Lightweight probabilistic texture retrieval. IEEE Transactions on Image Processing 19 ((1):):241–53. doi:10.1109/TIP.2009.2032313.

 PubMed Web of Science ®Google Scholar

Kokare, M., P. Biswas, and B. Chatterji. 2005. Texture image retrieval using new rotated complex wavelet filters. IEEE Transactions on System Management Cybern 35 ((6):):1168–78. doi:10.1109/TSMCB.2005.850176.

 PubMed Web of Science ®Google Scholar

Lowe, D. 2004. Distinctive image features from scale-invariant keypoints. Computer Vision 60 (2):91–110. doi:10.1023/B:VISI.0000029664.99615.94.

 Web of Science ®Google Scholar

Guo, Z., L. Zhang, and D. Zhang. 2010. Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern Recognition 43 ((3):):706–19. doi:10.1016/j.patcog.2009.08.017.

 Web of Science ®Google Scholar

Srivastava, P., and A. Khare. 2017. Integration of wavelet transform, local binary patterns and moments for content based image retrival. Visual Comminication and Image Representation 42:78–103. doi:10.1016/j.jvcir.2016.11.008.

 Web of Science ®Google Scholar

Zeinali, B., A. Ayatollahi, and M. Kakooei. 2014. A novel method of applying directional filter bank (DFB) for finger-knuckle-print (FKP) recognition. In: Electrical engineering, 22th iranian conference, (pp. 500–04) Tehran, Iran.

 Google Scholar

Meng, F., M. Song, B. Guo, R. Shi, and D. Shan. 2016. Image fusion based on object region detection and non-subsampled contourlet transform. Computers & Electrical Engineering 62: (1–9). doi: 10.1016/j.compeleceng.2016.09.019.

 Google Scholar

Guo, J., H. Prasetyo, and H. Su. 2013. Image indexing using the color and bit pattern feature fusion. Journal of Vision Communication and Image Representation 24 ((8):):1360–79. doi:10.1016/j.jvcir.2013.09.005.

 Web of Science ®Google Scholar

Kwitt, R., and A. Uhl. 2008. Image similarity measurement by Kullback-Leibler divergences between complex wavelet subband statistics for texture retrieval. IEEE International Conference on Image Processing, (pp. 933–36) San Diego, CA, USA.

 Google Scholar

Paschos, G., and M. Petrou. 2003. Histogram ratio features for color texture classifiction. Pattern Recognition Letters 24 ((1–3):):309–14. doi:10.1016/S0167-8655(02)00244-1.

 Web of Science ®Google Scholar

Liu, L., Y. Long, P. W. Fieguth, S. Lao, and G. Zhao. 2014. BRINT: Binary rotation invariant and noise tolerant texture classification. IEEE Transactions on Image Processing 23 ((7):):3071–84. doi:10.1109/TIP.2014.2325777.

 PubMed Web of Science ®Google Scholar

Bianconi, F., A. Fernandez, E. Gonzalez, D. Caride, and A. Calvino. 2009. Rotation-invariant colour texture classification through multilayer CCR. Pattern Recognition Letters 30 ((8):):765–73. doi:10.1016/j.patrec.2009.02.006.

 Web of Science ®Google Scholar

Pan, Z., Z. Li, H. Fan, and X. Wu. 2017. Feature based local binary pattern for rotation invariant texture classification. Expert Systems with Applications 88: (238–248):238–48. doi:10.1016/j.eswa.2017.07.007.

 Google Scholar

Hoang, M. A., J. M. Geusebroek, and A. W. M. Smeulders. 2005. Color texture measurement and segmentation. Signal Processing 85 ((2):):265–75. doi:10.1016/j.sigpro.2004.10.009.

 Web of Science ®Google Scholar

Junior, J. J., P. C. Cortex, and A. R. Backes. 2014. Color texture classification using shortest paths in graphs. IEEE Transactions on Image Processing 23 ((9):):3751–61. doi:10.1109/TIP.2014.2333655.

 PubMed Web of Science ®Google Scholar

Campana, B. J. L., and E. J. Keogh. 2010. A compression-based distance measure for texture. Statistical Analysis and Data Mining 3 (6):381–98. doi:10.1002/sam.10093.

 Google Scholar

Guha, T., and R. K. Ward. 2014. Image similarity using sparse representation and compression distanse. IEEE Transaction on Multimedia 16 ((4):):980–87. doi:10.1109/TMM.2014.2306175.

 Web of Science ®Google Scholar

Liu, G., H.,Z. Lei, Y. Xu.. 2011.Image retrieval based on micro-structure descriptor. Pattern Recognition Letters 44(9):2123–2133. doi:10.1016/j.patcog.2011.02.003.

 Google Scholar

Chen, J., S. Shan, C. He, G. Zhao, M. Pietikainen, X. Chen, and W. Gao. 2010. WLD: A robust local image descriptor. IEEE Transaction on Pattern Analysis and Machine Inteligence 32 ((9):):1705–20. doi:10.1109/TPAMI.2009.155.

 PubMed Web of Science ®Google Scholar

Rios, A. G., S. Tabik, J. Luengo, and A. S. M. Shihavuddin. 2019. Coral species identification with texture or structure images using a two-level classifier based on convolutional neural networks. Knowledge-Based Systems 184: (1–10). doi: 10.1016/j.knosys.2019.104891.

 Google Scholar

Ramteke, R., and K. Monali. 2012. Automatic medical image classification and abnormality detection using K nearest neighbour. Advanced Computer Research 2:190–96.

 Google Scholar

Subrahmanyam, M., R. Maheswari, and R. Balasubramanian. 2012. Local maximum edge binary patterns: A new descriptor for image retrieval and object tracking. Signal Processing 92 ((6):):1467–79. doi:10.1016/j.sigpro.2011.12.005.

 Web of Science ®Google Scholar

Porebski, A., N. Vandenbroucke, and L. Macaire. 2008. Haralick feature extraction from LBP images for color texture classification. In Proceedings of the 1st Workshops on Image Processing Theory, Tools and Applications, (pp. 1–8) Sousse, Tunisia.

 Google Scholar